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Original research
Magnetic resonance imaging-radiomics in endometrial cancer: a systematic review and meta-analysis
  1. Violante Di Donato1,
  2. Evangelos Kontopantelis2,
  3. Ilaria Cuccu1,
  4. Ludovica Sgamba1,
  5. Tullio Golia D'Augè1,
  6. Angelina Pernazza3,
  7. Carlo Della Rocca3,
  8. Lucia Manganaro4,
  9. Carlo Catalano4,
  10. Giorgia Perniola1,
  11. Innocenza Palaia1,
  12. Federica Tomao1,
  13. Andrea Giannini5,
  14. Ludovico Muzii1 and
  15. Giorgio Bogani6
  1. 1Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
  2. 2Division of Informatics, Imaging and Data Science, The University of Manchester, Manchester, UK
  3. 3Department of Medical-Surgical Sciences and Biotechnologies, University of Rome Sapienza, Rome, Italy
  4. 4Department of Radiological, Oncological and Pathological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
  5. 5Department of Medical and Surgical Sciences and Translational Medicine, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
  6. 6Department of Gynecologic Oncology, IRCCS National Cancer Institute, Milan, Italy
  1. Correspondence to Dr Ilaria Cuccu, Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome La Sapienza, Viale del Policlinico 155, 00161 Rome, Italy; ilaria.cuccu{at}uniroma1.it

Abstract

Objective Endometrial carcinoma is the most common gynecological tumor in developed countries. Clinicopathological factors and molecular subtypes are used to stratify the risk of recurrence and to tailor adjuvant treatment. The present study aimed to assess the role of radiomics analysis in pre-operatively predicting molecular or clinicopathological prognostic factors in patients with endometrial carcinoma.

Methods Literature was searched for publications reporting radiomics analysis in assessing diagnostic performance of MRI for different outcomes. Diagnostic accuracy performance of risk prediction models was pooled using the metandi command in Stata.

Results A search of MEDLINE (PubMed) resulted in 153 relevant articles. Fifteen articles met the inclusion criteria, for a total of 3608 patients. MRI showed pooled sensitivity and specificity 0.785 and 0.814, respectively, in predicting high-grade endometrial carcinoma, deep myometrial invasion (pooled sensitivity and specificity 0.743 and 0.816, respectively), lymphovascular space invasion (pooled sensitivity and specificity 0.656 and 0.753, respectively), and nodal metastasis (pooled sensitivity and specificity 0.831 and 0.736, respectively).

Conclusions Pre-operative MRI-radiomics analyses in patients with endometrial carcinoma is a good predictor of tumor grading, deep myometrial invasion, lymphovascular space invasion, and nodal metastasis.

  • Endometrial Neoplasms
  • Lymph Nodes
  • Sentinel Lymph Node
  • Uterine Cancer
  • Uterine Neoplasms

Data availability statement

All data relevant to the study are included in the article or uploaded as supplementary information. Further data are available upon reasonable request.

http://dx.doi.org/10.1136/ijgc-2023-004313

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    Data availability statement

    All data relevant to the study are included in the article or uploaded as supplementary information. Further data are available upon reasonable request.

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    Footnotes

    • Contributors VDD: conceptualization, methodology, software, validation, formal analysis, writing - original draft, visualization, supervision,

      guarantor, project administration. EK: methodology software, validation, formal analysis, visualization, supervision. IC: investigation, resources, data curation, writing - original draft, visualization. LS: investigation, resources, data curation, writing - original draft, visualization. TGD: investigation, data curation, writing - original draft, visualization. AP: validation, data curation, visualization. CDR: validation, data curation, visualization. LM: validation, data curation, visualization. CC: validation, data curation, visualization. GP: validation, data curation, visualization. IP: validation, data curation, visualization. FT: validation, data curation, visualization. AG: validation, data curation, visualization. supervision. LM: validation, supervision, project administration. GB: conceptualization, methodology, validation, formal analysis, writing - original draft, visualization, supervision, project administration.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.